Canonical Pathways Rewiring in Alzheimer’s Disease

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by the simultaneous disruption of interconnected molecular pathways, yet the structural mechanisms underlying this transcriptional disintegration remain poorly characterized. To address this, we constructed condition-specific gene co-expression networks from DLPFC bulk RNA-seq data, using a mutual-information (MI) framework with infomap community partitioning. Functional enrichment of network communities via Ingenuity Pathway Analysis (IPA) identified GABAergic signaling, SNARE complex assembly, Synaptogenesis, and neurexin and neuroligin interactions as significantly overrepresented pathways. Integration of node degree with condition-specific average expression revealed coordinated topological centralization of key synaptic genes—including NRXN2, LRRTM1, DLGAP3, and SHANK1—alongside a widespread transcriptional downregulation in GABAergic and Synaptogenesis modules. A shortest-path analysis revealed a consistent expansion of intra-pathway distances across all evaluated canonical pathways in AD, a pattern statistically consistent with reduced local co-expression cohesion. These findings reframe Late-Onset Alzheimer’s Disease (LOAD) as an active structural-rewiring process, in which the observed topological centralization pattern seems to be consistent with a consolidation of co-expression around synaptic components, though we cannot exclude that shifts in cellular composition contribute to this signal in bulk RNA-seq data.

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Publication Details

Journal
International Journal of Molecular Sciences
Published
2026-05-27
DOI
https://doi.org/10.3390/ijms27114835
Primary Topic
Bioinformatics and Genomic Networks
Type
article
Field-Weighted Citation Impact
0.00

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article

Canonical Pathways Rewiring in Alzheimer’s Disease

Guillermo de Anda‐Jáuregui, Alejandra Paulina Pérez-González, Enrique Hernández-Lemus, Alejandro Pinta-Castro et al.
International Journal of Molecular Sciences
Bioinformatics and Genomic Networks
article

Canonical Pathways Rewiring in Alzheimer’s Disease

Guillermo de Anda‐Jáuregui, Alejandra Paulina Pérez-González, Enrique Hernández-Lemus, Alejandro Pinta-Castro, Gabriela Michel-Ureña
article en

Abstract

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by the simultaneous disruption of interconnected molecular pathways, yet the structural mechanisms underlying this transcriptional disintegration remain poorly characterized. To address this, we constructed condition-specific gene co-expression networks from DLPFC bulk RNA-seq data, using a mutual-information (MI) framework with infomap community partitioning. Functional enrichment of network communities via Ingenuity Pathway Analysis (IPA) identified GABAergic signaling, SNARE complex assembly, Synaptogenesis, and neurexin and neuroligin interactions as significantly overrepresented pathways. Integration of node degree with condition-specific average expression revealed coordinated topological centralization of key synaptic genes—including NRXN2, LRRTM1, DLGAP3, and SHANK1—alongside a widespread transcriptional downregulation in GABAergic and Synaptogenesis modules. A shortest-path analysis revealed a consistent expansion of intra-pathway distances across all evaluated canonical pathways in AD, a pattern statistically consistent with reduced local co-expression cohesion. These findings reframe Late-Onset Alzheimer’s Disease (LOAD) as an active structural-rewiring process, in which the observed topological centralization pattern seems to be consistent with a consolidation of co-expression around synaptic components, though we cannot exclude that shifts in cellular composition contribute to this signal in bulk RNA-seq data.

International Journal of Molecular SciencesVol. 27(11)
Universidad La Salle (BO), National Institute of Genomic Medicine (MX), Secretaría de Ciencia Tecnología e Innovación (MX), Universidad Mexicana (MX), Universidad La Salle (MX), Instituto de Medicina Genómica (ES), Universidad Nacional Autónoma de México (MX)
Secretaría de Ciencia, Tecnología e Innovación del Distrito Federal
Openalex Percentile: Top 9%
Bioinformatics and Genomic Networks
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